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This document discusses artificial intelligence (AI) in healthcare, including key challenges and best practices for implementation. Some common challenges with AI implementation include not having enough high quality data for training models and ensuring the models align with real-world problems that can change over time. It is important to have a planned strategy for AI, carefully select partners, and ensure ethical and transparent use of data that complies with regulations. When implemented properly, AI has potential to improve healthcare through applications like personalized patient experiences and optimizing operations.
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- 2. Key Takeaways This paper is intended to answer the following questions – • Are we ready for artificial intelligence (AI)? • AI framework and existing data ecosystem - What are the most common AI implementation challenges? - What are the standard best practices that should be in place? • Future trends and opportunities for AI in Healthcare (case studies) • Recommendations – - For technology developers - For investors - For end-users Executive Overview www.infoholicresearch.com Leading research and innovation hub for AI Market Dynamics that make these countries more lucrative for AI based research and development 1. U.S. – hub of technology evolution, with over 1000+ AI companies (including start-ups and tech giants) and over US$10 billion in venture capital funds for R&D 2. Germany – Decline in working population and high propensity of automation, of close to 50% potential estimates across industries 3. U.K. – Studies reveal AI implementation in businesses across U.K have a potential to boost productivity by about 30%, bolstering huge investments in collaborative AI research with leading universities and technology compa- nies across the country 4. China – High penchant to technology development with strong government patronage to become an “economic power” backed by skill development and research in technological breakthroughs. China has produced almost twice as many research papers in AI as compared to the U.S. 5. Japan – Dwindling available workforce, highly prone to natural calamities leading to high propensity for automa- tion
- 4. Artificial Intelligence Readiness www.infoholicresearch.com For one moment, let’s all assume to be privileged with equal exposure to a set of extremely efficient and desirable AI platforms, ready to be tailored as per our business requi- sites. Now, to understand and analyse an organization’s AI readiness, the following questions are few of the areas that should be taken into considerations – • What are the top 2 priorities that your company has or would be willing to invest in, as a part of its AI based digital transformation initiatives? • Is your organization already using AI driven approach for key business functionalities such as marketing cam- paigns? If yes, what are the key aspects of this initiative that you believe is making a difference in creating new growth areas for your organization? (choose the relevant options) o Real-time customer support across multiple channels or touchpoints o Understanding behaviour patterns to personalise and enhance customer experience o Understanding behaviour patterns to identifying new customers o Identify and analyse key customer pain points (from emotional cues) to add new/ enhance existing service offerings o Analyse and score key internal performance metrics o To manage and optimize internal operations across business functions • On a scale of (1-5, 5 – highest/strongly agree, 1 – lowest/completely disagree, 3 - don’t know /indiffe ent/not sure) Do you have the right people and skill sets to define the AI roadmap for your organization? • On a scale of (1-5, 5 being highest) How clearly is your AI based digital transformation strategy articulated? • On a scale of (1-5, 5 being highest) How competent is your workforce in understanding the value proposition of AI in context to – setting up a seamless integration between the set customer expectations and across the multichannel client facing environments/ interfaces? • On a scale of (1-5, 5 being highest) How competent is your organization in measuring and clearly articulating the business results of the existing artificial intelligence marketing strategy? • On a scale of (1-5, 5 - highest/strongly agree; 1 - lowest/completely disagree; & 3 - don’t know /indifferent/not sure) How prepared is your organiza- tion’s structure and cultural integrity, in terms of taking a strong transformational step towards creating an agile organizational structure (essential for an effective cross functional collaboration within an AI augmented work- flow)? • On a scale of (1-5, 5 being highest) How clearly have you identified the redundant processes for your marketing programs that can be handled by your AI based digital transformation roadmap? • On a scale of (1-5, 5 - highest/strongly agree; 1 - lowest/completely disagree; & 3 - don’t know /indif- ferent/not sure) Does you company have a dynamic and scalable technology budget for shifting priorities such as moving to agile and scalable cloud-based infrastructure or investing in multichannel digital experience platform to support AI driven digital transformation? The chart below showcases the AI readiness of organizations globally based on multiple surveys done across a balanced spread of large MNCs, small & medium enterprises, and start-ups (Note: this data is for organizations across indus- tries and not specific to healthcare – FYI: healthcare has shown significantly higher readiness than most other indus- tries). The results clearly show that predominantly most com- panies (categorized as opportunistic) are well balanced to move to the next stage of AI digital evolution, based on their vision, operations, skill sets, and their existing digital infra- structure. This might happen in the near-future or over a period-of-time which will be discussed briefly in the next section. So, each of the categories have their own set of pros and cons in the path of AI driven digital transformation roadmap. However, before understanding each category in further detail we should understand AI and the important role of data in this transformational journey. 25% 75% 5%
- 5. AI Framework And Data Ecosystem www.infoholicresearch.com The illustration of the AI framework above, highlights the significance of data ingested into the system for generating reliable and actionable insights. With that said, it can be inferred that data is as important as the algorithms itself and sometimes even more. The machine learning or deep learning algorithms are trained based on several parameters which are data intensive. So, a good training, is invariably dependent on the quality and quantity of data fed, for the process of tuning the system to perform desired functional- ities. Again, as the current business environment is transient, there is constant accrual of new data that can significantly change the desired purview of a given scenario that is being analysed. So, these changes in data should be carefully ingested and classified which can sometimes generate interesting patterns. These patterns reveal crucial informa- tion that can help organizations to step ahead of their competitors in terms of providing enhanced customer experience. Key Takeaways • Data holds the key to AI adoption roadmap • Abundance of data – mostly dark data • Reliable sourcing and governance of data are critical to successful AI implementation Concepts are vindicated by the constant accrual of data and indepen- dent verification of data Stanley Benjamin Prusiner (M.D., neurologist and biochemist) “ It is a capital mistake to theorize before one has data Arthur Conan Doyle (well-known British writer) “ Good policy is grounded in a robust set of facts and data Seth Wilbur Moulton (American politician) “ Data is valuable, so let's be mindful of how we're sharing it Clara Shih (CEO and co-founder of Hearsay Social) “ Now, this brings us to another aspect of data in this modern digital environment, where we have huge amounts of data in various formats, which can be structured, unstructured or semi-structured. However, unfortunately, still about 80% of the data present is in the form of dark data, which is either not being captured or we do not have the tools and/or right skill sets to capture and analyse the same. This presents a huge opportunity for exploring new frontiers with AI with increasing computing capabilities and big data analytics capabilities
- 6. Common AI implementation challenges! Best Practices and Regulations www.infoholicresearch.com The AI evolution concept can be related to the way a child learns. Wrong concepts and misguidance is unfavourable for the child’s future while lack of education or training leads to blissful ignorance! Likewise, the training and/or learning process of the machines should be continuous, with data scientists governing the quality of data being fed. #1 Now this brings in the questions – do we know what data and quantity of data that needs to be ingested for the training process? Do we have enough data scientists to govern the process? Though repeated experiments are being done to automate this process using neural networks which sounds wonderful, however in theory and in real world application it is extreme- ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series analysis and when you need to automate the thinking process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much faster and possibly more accurate). #2 So then, this brings to our next set of questions – who conforms to our understanding of a real-life problem as certain problems are relative to individual perceptions? Now, supposedly if we align our understanding and design our AI strategy for a given set of problems, the above concern could be resolved. However, as we know that most pertinent problems statements and the real-world scenarios for a competitive business environment are transient. Often by the time the problems are resolved, we are intrigued by a new set of mind boggling mysteries to unravel. Now, AI experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego- rised with well-defined tags that enable the algorithms to build patterns over a course of period. #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning future patterns? Considering that the business-critical functions have been identified, then the processes that have imminent risk of failure, need to be worked upon. For this to be achieved, we can leverage AI driven automation and set up bots which can crawl across numerous functional paths of an organiza- tion’s digital footprint to highlight mission-critical functions and thereby enable similar incidents to be uncovered which percolates to pattern identification for predicting future anomalies/variance from existing rules. Knowingly or unknowingly most of us are already being influenced by the implications of AI in our daily lives. Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI in recent years. In fact, over the past 2 years, technology companies have taken substantial strides in manifesting plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: • Planned implementation of AI strategy (not just following peers) • Careful selection of partners in AI journey • Thorough understanding of data • Transparent and ethical AI implementation strategy As AI promises limitless possibilities for organizations with immediate ROI being realized through its benefits in the field of marketing, it is essential to always align the strategic implementation of AI with clear set ethical boundaries. There have already been some well-known instances of organizations having crossed the moral obligation that ownership of data presumably infoliates. With this in perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best practices – • GDPR (General Data Protection Regulation) – 2018 • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) • Sarbanes-Oxley (SOX) Act – 2002 • Federal Information Security Management Act (FISMA) – 2002 • HIPAA (Health Insurance Portability and Accountability Act) – 1996 #4 AI could disrupt the foundations of the digital transforma- tional journey with unclear future implication. Most transformational journeys, the classic example being the introduction of computers, have created an uproar of impeding employment for masses. However, most technolo- gies have evolved in creating more opportunities along its way of evolution. Similarly, AI could potentially be a boon for mankind in its path of evolution or on the contrary - as predicted by Stephen Hawking: “replace humans altogether”. Now, that statement as of now, may seem outrageous but do we know for sure? • Planned implementation of AI strategy (not just following peers) • Planned implementation of AI strategy (not just following peers) • Careful selection of partners in AI journey consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much consider that the AI models should already know the things that we know. 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So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: • Planned implementation of AI strategy plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI in recent years. In fact, over the past 2 years, technology companies have taken substantial strides in manifesting plethora of commercial applications of AI. So, in the build-up Best Practices and Regulations Knowingly or unknowingly most of us are already being influenced by the implications of AI in our daily lives. Though the foundations of AI dates back to 1950s, however companies have taken substantial strides in manifesting plethora of commercial applications of AI. So, in the build-up this process using neural networks which sounds wonderful, however in theory and in real world application it is extreme ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series this process using neural networks which sounds wonderful, however in theory and in real world application it is extreme ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series analysis and when you need to automate the thinking process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own Though repeated experiments are being done to automate this process using neural networks which sounds wonderful, however in theory and in real world application it is extreme ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series analysis and when you need to automate the thinking Best Practices and RegulationsBest Practices and Regulations this process using neural networks which sounds wonderful, however in theory and in real world application it is extreme ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series analysis and when you need to automate the thinkinganalysis and when you need to automate the thinking process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being muchanalyse things (with the benefit limited to only being much that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much faster and possibly more accurate). #2 So then, this brings to our next set of questions – who#2 So then, this brings to our next set of questions – who conforms to our understanding of a real-life problem as certain problems are relative to individual perceptions? Now, supposedly if we align our understanding and design our AI strategy for a given set of problems, the above #2 So then, this brings to our next set of questions – who conforms to our understanding of a real-life problem as certain problems are relative to individual perceptions? Now, supposedly if we align our understanding and design our AI strategy for a given set of problems, the above concern could be resolved. However, as we know that most process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much influenced by the implications of AI in our daily lives.influenced by the implications of AI in our daily lives. that we know. This sound ok, but we need to also realise that Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI in recent years. In fact, over the past 2 years, technology companies have taken substantial strides in manifesting plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: Though repeated experiments are being done to automate this process using neural networks which sounds wonderful, however in theory and in real world application it is extreme ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series analysis and when you need to automate the thinking however in theory and in real world application it is extreme Best Practices and Regulations Knowingly or unknowingly most of us are already being influenced by the implications of AI in our daily lives. Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI inmost of us have started to realise the significance of AI in recent years. In fact, over the past 2 years, technology companies have taken substantial strides in manifesting plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: • Planned implementation of AI strategy plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: • Planned implementation of AI strategy Best Practices and Regulations Knowingly or unknowingly most of us are already being influenced by the implications of AI in our daily lives. Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI in Best Practices and Regulations Knowingly or unknowingly most of us are already being influenced by the implications of AI in our daily lives. Though the foundations of AI dates back to 1950s, however recent years. In fact, over the past 2 years, technology companies have taken substantial strides in manifesting plethora of commercial applications of AI. So, in the build-up Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI in recent years. In fact, over the past 2 years, technologyprocess with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series analysis and when you need to automate the thinking process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own #2 So then, this brings to our next set of questions – who conforms to our understanding of a real-life problem as certain problems are relative to individual perceptions? Now, supposedly if we align our understanding and design faster and possibly more accurate). #2 So then, this brings to our next set of questions – who conforms to our understanding of a real-life problem as certain problems are relative to individual perceptions? Now, supposedly if we align our understanding and design real-life problems are complex and require non-linear regression. These problems mostly incorporate time series • Careful selection of partners in AI journey plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: this process using neural networks which sounds wonderful, however in theory and in real world application it is extreme Best Practices and Regulationstake in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most regression. These problems mostly incorporate time series analysis and when you need to automate the thinking process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analyse things (with the benefit limited to only being much faster and possibly more accurate). #2 So then, this brings to our next set of questions – who conforms to our understanding of a real-life problem asconforms to our understanding of a real-life problem as certain problems are relative to individual perceptions? process with high speed computation, then we need to consider that the AI models should already know the thingsconsider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our ownthe model is limited to the conceptual modelling of our own understanding and thus limited to the extent of how you analysis and when you need to automate the thinking process with high speed computation, then we need to consider that the AI models should already know the things that we know. This sound ok, but we need to also realise that the model is limited to the conceptual modelling of our own • Planned implementation of AI strategy plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful plethora of commercial applications of AI. So, in the build-up of this AI propaganda, organizations need to be careful about some of the following best practices: • Planned implementation of AI strategy recent years. In fact, over the past 2 years, technology companies have taken substantial strides in manifesting Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI in recent years. In fact, over the past 2 years, technology ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be Best Practices and Regulations ly complex. The reason being, most theoretical problems take in a lot of estimations and assumptions, which can be easily solved with linear regression analysis. However, most real-life problems are complex and require non-linear regression. These problems mostly incorporate time series analysis and when you need to automate the thinking Best Practices and Regulations Knowingly or unknowingly most of us are already being influenced by the implications of AI in our daily lives. Though the foundations of AI dates back to 1950s, however most of us have started to realise the significance of AI in recent years. In fact, over the past 2 years, technology companies have taken substantial strides in manifesting plethora of commercial applications of AI. So, in the build-up #2 So then, this brings to our next set of questions – who conforms to our understanding of a real-life problem as certain problems are relative to individual perceptions? Now, supposedly if we align our understanding and design our AI strategy for a given set of problems, the above concern could be resolved. However, as we know that most pertinent problems statements and the real-world scenarios for a competitive business environment are transient. Often by the time the problems are resolved, we are intrigued by a new set of mind boggling mysteries to unravel. Now, AI experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego rised with well-defined tags that enable the algorithms to #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning for a competitive business environment are transient. Often by the time the problems are resolved, we are intrigued by a instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego- #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning Considering that the business-critical functions have been identified, then the processes that have imminent risk of pertinent problems statements and the real-world scenarios for a competitive business environment are transient. Often There have already been some well-known instances of organizations having crossed the moral obligation that ownership of data presumably infoliates. With this in perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best practices – (General Data Protection Regulation) – 2018 • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) Sarbanes-Oxley • Federal Information Security Management Act (FISMA) – 2002 implementation of AI with clear set ethical boundaries. Considering that the business-critical functions have been failure, need to be worked upon. For this to be achieved, we that organizations have to comply, as a part of their AI best practices – • GDPR • Payment Card Industry Data Security Standard • perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best (PCI-DSS) – version V1 2004 (latest version in 2018) Sarbanes-Oxley • Federal Information Security Management Act (FISMA) – 2002 • HIPAA Act) – 1996 • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) that organizations have to comply, as a part of their AI best (General Data Protection Regulation) – 2018 • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) (SOX) Act – 2002 • Federal Information Security Management Act (Health Insurance Portability and Accountability perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best There have already been some well-known instances of organizations having crossed the moral obligation that ownership of data presumably infoliates. With this in perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best (General Data Protection Regulation) – 2018 • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) (SOX) Act – 2002 • Federal Information Security Management Act implementation of AI with clear set ethical boundaries. There have already been some well-known instances ofThere have already been some well-known instances of perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best (General Data Protection Regulation) – 2018 (PCI-DSS) – version V1 2004 (latest version in 2018) organizations having crossed the moral obligation that ownership of data presumably infoliates. With this in perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best (General Data Protection Regulation) – 2018 • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) (SOX) Act – 2002 • Federal Information Security Management Act There have already been some well-known instances of organizations having crossed the moral obligation that that organizations have to comply, as a part of their AI best (General Data Protection Regulation) – 2018 • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) • Federal Information Security Management Act (Health Insurance Portability and Accountability perspective there are a few set standards and regulations that organizations have to comply, as a part of their AI best (PCI-DSS) – version V1 2004 (latest version in 2018) • Federal Information Security Management Act (Health Insurance Portability and Accountability • Payment Card Industry Data Security Standard (PCI-DSS) – version V1 2004 (latest version in 2018) for a competitive business environment are transient. Often by the time the problems are resolved, we are intrigued by a new set of mind boggling mysteries to unravel. Now, AI experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego rised with well-defined tags that enable the algorithms to build patterns over a course of period. #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning pertinent problems statements and the real-world scenarios for a competitive business environment are transient. Oftenfor a competitive business environment are transient. Often by the time the problems are resolved, we are intrigued by a new set of mind boggling mysteries to unravel. Now, AI experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego rised with well-defined tags that enable the algorithms to build patterns over a course of period. #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning Considering that the business-critical functions have been identified, then the processes that have imminent risk of failure, need to be worked upon. For this to be achieved, we pertinent problems statements and the real-world scenarios for a competitive business environment are transient. Often by the time the problems are resolved, we are intrigued by a new set of mind boggling mysteries to unravel. Now, AI experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego rised with well-defined tags that enable the algorithms to build patterns over a course of period. #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning future patterns? Considering that the business-critical functions have been identified, then the processes that have imminent risk of failure, need to be worked upon. For this to be achieved, we for a competitive business environment are transient. Often by the time the problems are resolved, we are intrigued by a instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego rised with well-defined tags that enable the algorithms to build patterns over a course of period. #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning future patterns? Considering that the business-critical functions have been identified, then the processes that have imminent risk of failure, need to be worked upon. For this to be achieved, we can leverage AI driven automation and set up bots which can crawl across numerous functional paths of an organiza tion’s digital footprint to highlight mission-critical functions and thereby enable similar incidents to be uncovered which experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the first question – do we have enough data to start learning Considering that the business-critical functions have been identified, then the processes that have imminent risk of failure, need to be worked upon. For this to be achieved, we can leverage AI driven automation and set up bots which can crawl across numerous functional paths of an organiza tion’s digital footprint to highlight mission-critical functions and thereby enable similar incidents to be uncovered which percolates to pattern identification for predicting future anomalies/variance from existing rules. #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego rised with well-defined tags that enable the algorithms to build patterns over a course of period. #3 Well, this sounds great! However, it brings me back to the first question – do we have enough data to start learning Considering that the business-critical functions have been identified, then the processes that have imminent risk of failure, need to be worked upon. For this to be achieved, we can leverage AI driven automation and set up bots which can crawl across numerous functional paths of an organiza experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the new set of mind boggling mysteries to unravel. Now, AI experts have a solution to this problem, where each new instance of problem can be ingested as a new incident in the time series. Moreover, each similar incident can be catego rised with well-defined tags that enable the algorithms to by the time the problems are resolved, we are intrigued by a new set of mind boggling mysteries to unravel. Now, AI
- 7. www.infoholicresearch.com Healthcare Applications More Data for improved ability to Specialize: AI needs more training data for better interference and results, as almost all AI efforts are limited by data available. Blockchain can be used to publish metada- ta that exists across a consortium of healthcare organizations. This metada- ta can include pointers to the enterprise systems that store the data, and hash- codes that can be used to verify the integrity of data. AI-assisted Robotic Surgery Surgical robots can analyse data from pre-op medical records to guide a surgeon's instrument during surgery, which can lead to a 21% reduction in a patient's hospital stay. Medical robots can use data from past operations via AI to inform new surgical techniques. AI-assisted robotic procedure is resulting in approximate five times fewer complica- tions compared to surgeons operating alone Improvisation in Nurse Call Systems Most applications of virtual nursing assistants in the developed countries are regularizing communication between patients and care providers to prevent hospital readmission or unnecessary hospital visits. From interacting with patients to directing patients to the most effective care setting, virtual nursing assistants could save the healthcare industry an estimate of $20 billion annually. Since virtual nurses are available 24/7, they can answer questions, monitor patients and provide quick answers. Application in Telemedi- cine and Image Analysis Recently, an MIT-led research team developed a machine-learning algorithm that can analyze 3D scans up to 1,000 times faster than what is possible today. This near real-time assess- ment can provide critical input for surgeons who are operating. Moreover, it is expected that AI can help to improve the next generation of radiology tools that don’t rely on tissue samples in the future. Further, AI image analysis could support remote areas that don’t have easy access to healthcare providers and even make telemedicine more effective. AI applications in clinical health can raise more than $150 billion annual savings for the US healthcare economy by 2026. AI application has incredible potential in healthcare including diagnostic imaging, anti-fraud, resource and asset optimization, readmission prevention, behavioural analytics, medical risk analytics, claims analytics, and many more. Major opportunities of AI in Healthcare industry are pointed below: This confluence of technology-based products, platforms and solutions is leading to a previously unimagined precision medicine, down to the familiar and individual level, which one day may even be able to predict and there- by prevent disease. However, healthcare AI is still in its infant state, as the transformation from existing technology is complex and time consuming. It also involves a lot of training of healthcare practitioners which can be challenging. The entire process, starting from complex data collection and curation procedures for relevant AI applications in healthcare to concerns about job loss, are some of the few road blocks due to which healthcare providers have been skeptical to jump on board this AI journey. However, eventually as the healthcare industry continues to turn to a value-based care model, it’s easy to believe that providers who utilize and fully understand the unique capabilities of AI solutions will perform above the rest and set the roadmap for AI adoption in the healthcare sector.